learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_HF with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
num_epochs: 10

Training Loss	Epoch	Step	Validation Loss
6.2845	0.3137	100	1.7300
0.9773	0.6275	200	0.6286
0.5181	0.9412	300	0.4802
0.4126	1.2549	400	0.4208
0.365	1.5686	500	0.3774
0.3369	1.8824	600	0.3645
0.3037	2.1961	700	0.3299
0.2854	2.5098	800	0.3164
0.2939	2.8235	900	0.3093
0.2547	3.1373	1000	0.2961
0.2275	3.4510	1100	0.2943
0.2456	3.7647	1200	0.2824
0.2368	4.0784	1300	0.2723
0.2148	4.3922	1400	0.2733
0.2118	4.7059	1500	0.2737
0.1991	5.0196	1600	0.2715
0.1879	5.3333	1700	0.2657
0.1841	5.6471	1800	0.2746
0.1912	5.9608	1900	0.2642
0.1509	6.2745	2000	0.2964
0.1818	6.5882	2100	0.2607
0.1736	6.9020	2200	0.2644
0.1618	7.2157	2300	0.2663
0.1563	7.5294	2400	0.2637
0.159	7.8431	2500	0.2561
0.1488	8.1569	2600	0.2554
0.1417	8.4706	2700	0.2589
0.1329	8.7843	2800	0.2599
0.1455	9.0980	2900	0.2589
0.1521	9.4118	3000	0.2576
0.1334	9.7255	3100	0.2568

seyviour/paligemma_vqav2

paligemma_vqav2